Models / kimi_linear

Kimi-Linear-48B-A3B-Instruct

Reconstructed from its own config.json with no weights read. 177K downloads on Hugging Face.

Our count against the checkpoint

The left number comes from the graph. The right one is the number of scalars in the published weight files. Nothing on this page was tuned to make them agree.

Derived from structure

48.70B

48,702,058,240 parameters

In the published checkpoint

49.12B

49,122,681,728 scalars · safetensors.total, read 2025-12-16

Delta

-0.86%

custom-code This repository ships its own modeling code (`auto_map`, e.g. `configuration_kimi.py`), so `config.json` names a class in the repo rather than an architecture `transformers` defines. The graph below is what those config keys mean under `transformers` semantics, which is not necessarily what the repo's own file builds. A gap here is a statement about what we read, not about the checkpoint.

What it costs to run

Cost is a roofline estimate on the priced GPU for 10 epochs at batch 32 over 50,000 samples (assumed; no dataset attached). GPU fit is fp32 weights plus gradients plus two Adam moments (16 bytes per parameter) with 1.3x headroom; activations are not included and grow with batch size.

Layers

166

Will it forward-pass

Yes

Priced on

A10G (24GB)

Est. one run

$52.13

Card	Memory
T4 (16GB)	weights + activations	does not fit
A100 (40GB)	weights + activations	does not fit
H100 (80GB)	weights + activations	does not fit

Structure

168 nodes. Output shapes are propagated from the input shape, batch dimension excluded.

	Layer	Type	Output shape
1	Input	Input	1 × 512
2	Embedding	Embedding	1 × 512 × 2304
3	RoPE	RoPE	1 × 512 × 2304
4	RMSNorm_1_1	RMSNorm	1 × 512 × 2304
5	Attention_1	Grouped Query Attn	1 × 512 × 2304
6	Add_1_attn	Add	1 × 512 × 2304
7	RMSNorm_1_2	RMSNorm	1 × 512 × 2304
8	FFN_1	SwiGLU	1 × 512 × 2304
9	Add_1_ffn	Add	1 × 512 × 2304
10	RMSNorm_2_1	RMSNorm	1 × 512 × 2304
11	Attention_2	Grouped Query Attn	1 × 512 × 2304
12	Add_2_attn	Add	1 × 512 × 2304
13	RMSNorm_2_2	RMSNorm	1 × 512 × 2304
14	MoE_2	Shared-Expert MoE	1 × 512 × 2304
15	Add_2_ffn	Add	1 × 512 × 2304
16	RMSNorm_3_1	RMSNorm	1 × 512 × 2304
17	Attention_3	Grouped Query Attn	1 × 512 × 2304
18	Add_3_attn	Add	1 × 512 × 2304
19	RMSNorm_3_2	RMSNorm	1 × 512 × 2304
20	MoE_3	Shared-Expert MoE	1 × 512 × 2304
21	Add_3_ffn	Add	1 × 512 × 2304
22	RMSNorm_4_1	RMSNorm	1 × 512 × 2304
23	Attention_4	Grouped Query Attn	1 × 512 × 2304
24	Add_4_attn	Add	1 × 512 × 2304
25	RMSNorm_4_2	RMSNorm	1 × 512 × 2304
26	MoE_4	Shared-Expert MoE	1 × 512 × 2304
27	Add_4_ffn	Add	1 × 512 × 2304
28	RMSNorm_5_1	RMSNorm	1 × 512 × 2304
29	Attention_5	Grouped Query Attn	1 × 512 × 2304
30	Add_5_attn	Add	1 × 512 × 2304
31	RMSNorm_5_2	RMSNorm	1 × 512 × 2304
32	MoE_5	Shared-Expert MoE	1 × 512 × 2304
33	Add_5_ffn	Add	1 × 512 × 2304
34	RMSNorm_6_1	RMSNorm	1 × 512 × 2304
35	Attention_6	Grouped Query Attn	1 × 512 × 2304
36	Add_6_attn	Add	1 × 512 × 2304
37	RMSNorm_6_2	RMSNorm	1 × 512 × 2304
38	MoE_6	Shared-Expert MoE	1 × 512 × 2304
39	Add_6_ffn	Add	1 × 512 × 2304
40	RMSNorm_7_1	RMSNorm	1 × 512 × 2304
41	Attention_7	Grouped Query Attn	1 × 512 × 2304
42	Add_7_attn	Add	1 × 512 × 2304
43	RMSNorm_7_2	RMSNorm	1 × 512 × 2304
44	MoE_7	Shared-Expert MoE	1 × 512 × 2304
45	Add_7_ffn	Add	1 × 512 × 2304
46	RMSNorm_8_1	RMSNorm	1 × 512 × 2304
47	Attention_8	Grouped Query Attn	1 × 512 × 2304
48	Add_8_attn	Add	1 × 512 × 2304
49	RMSNorm_8_2	RMSNorm	1 × 512 × 2304
50	MoE_8	Shared-Expert MoE	1 × 512 × 2304
51	Add_8_ffn	Add	1 × 512 × 2304
52	RMSNorm_9_1	RMSNorm	1 × 512 × 2304
53	Attention_9	Grouped Query Attn	1 × 512 × 2304
54	Add_9_attn	Add	1 × 512 × 2304
55	RMSNorm_9_2	RMSNorm	1 × 512 × 2304
56	MoE_9	Shared-Expert MoE	1 × 512 × 2304
57	Add_9_ffn	Add	1 × 512 × 2304
58	RMSNorm_10_1	RMSNorm	1 × 512 × 2304
59	Attention_10	Grouped Query Attn	1 × 512 × 2304
60	Add_10_attn	Add	1 × 512 × 2304
61	RMSNorm_10_2	RMSNorm	1 × 512 × 2304
62	MoE_10	Shared-Expert MoE	1 × 512 × 2304
63	Add_10_ffn	Add	1 × 512 × 2304
64	RMSNorm_11_1	RMSNorm	1 × 512 × 2304
65	Attention_11	Grouped Query Attn	1 × 512 × 2304
66	Add_11_attn	Add	1 × 512 × 2304
67	RMSNorm_11_2	RMSNorm	1 × 512 × 2304
68	MoE_11	Shared-Expert MoE	1 × 512 × 2304
69	Add_11_ffn	Add	1 × 512 × 2304
70	RMSNorm_12_1	RMSNorm	1 × 512 × 2304
71	Attention_12	Grouped Query Attn	1 × 512 × 2304
72	Add_12_attn	Add	1 × 512 × 2304
73	RMSNorm_12_2	RMSNorm	1 × 512 × 2304
74	MoE_12	Shared-Expert MoE	1 × 512 × 2304
75	Add_12_ffn	Add	1 × 512 × 2304
76	RMSNorm_13_1	RMSNorm	1 × 512 × 2304
77	Attention_13	Grouped Query Attn	1 × 512 × 2304
78	Add_13_attn	Add	1 × 512 × 2304
79	RMSNorm_13_2	RMSNorm	1 × 512 × 2304
80	MoE_13	Shared-Expert MoE	1 × 512 × 2304
81	Add_13_ffn	Add	1 × 512 × 2304
82	RMSNorm_14_1	RMSNorm	1 × 512 × 2304
83	Attention_14	Grouped Query Attn	1 × 512 × 2304
84	Add_14_attn	Add	1 × 512 × 2304
85	RMSNorm_14_2	RMSNorm	1 × 512 × 2304
86	MoE_14	Shared-Expert MoE	1 × 512 × 2304
87	Add_14_ffn	Add	1 × 512 × 2304
88	RMSNorm_15_1	RMSNorm	1 × 512 × 2304
89	Attention_15	Grouped Query Attn	1 × 512 × 2304
90	Add_15_attn	Add	1 × 512 × 2304
91	RMSNorm_15_2	RMSNorm	1 × 512 × 2304
92	MoE_15	Shared-Expert MoE	1 × 512 × 2304
93	Add_15_ffn	Add	1 × 512 × 2304
94	RMSNorm_16_1	RMSNorm	1 × 512 × 2304
95	Attention_16	Grouped Query Attn	1 × 512 × 2304
96	Add_16_attn	Add	1 × 512 × 2304
97	RMSNorm_16_2	RMSNorm	1 × 512 × 2304
98	MoE_16	Shared-Expert MoE	1 × 512 × 2304
99	Add_16_ffn	Add	1 × 512 × 2304
100	RMSNorm_17_1	RMSNorm	1 × 512 × 2304
101	Attention_17	Grouped Query Attn	1 × 512 × 2304
102	Add_17_attn	Add	1 × 512 × 2304
103	RMSNorm_17_2	RMSNorm	1 × 512 × 2304
104	MoE_17	Shared-Expert MoE	1 × 512 × 2304
105	Add_17_ffn	Add	1 × 512 × 2304
106	RMSNorm_18_1	RMSNorm	1 × 512 × 2304
107	Attention_18	Grouped Query Attn	1 × 512 × 2304
108	Add_18_attn	Add	1 × 512 × 2304
109	RMSNorm_18_2	RMSNorm	1 × 512 × 2304
110	MoE_18	Shared-Expert MoE	1 × 512 × 2304
111	Add_18_ffn	Add	1 × 512 × 2304
112	RMSNorm_19_1	RMSNorm	1 × 512 × 2304
113	Attention_19	Grouped Query Attn	1 × 512 × 2304
114	Add_19_attn	Add	1 × 512 × 2304
115	RMSNorm_19_2	RMSNorm	1 × 512 × 2304
116	MoE_19	Shared-Expert MoE	1 × 512 × 2304
117	Add_19_ffn	Add	1 × 512 × 2304
118	RMSNorm_20_1	RMSNorm	1 × 512 × 2304
119	Attention_20	Grouped Query Attn	1 × 512 × 2304
120	Add_20_attn	Add	1 × 512 × 2304
121	RMSNorm_20_2	RMSNorm	1 × 512 × 2304
122	MoE_20	Shared-Expert MoE	1 × 512 × 2304
123	Add_20_ffn	Add	1 × 512 × 2304
124	RMSNorm_21_1	RMSNorm	1 × 512 × 2304
125	Attention_21	Grouped Query Attn	1 × 512 × 2304
126	Add_21_attn	Add	1 × 512 × 2304
127	RMSNorm_21_2	RMSNorm	1 × 512 × 2304
128	MoE_21	Shared-Expert MoE	1 × 512 × 2304
129	Add_21_ffn	Add	1 × 512 × 2304
130	RMSNorm_22_1	RMSNorm	1 × 512 × 2304
131	Attention_22	Grouped Query Attn	1 × 512 × 2304
132	Add_22_attn	Add	1 × 512 × 2304
133	RMSNorm_22_2	RMSNorm	1 × 512 × 2304
134	MoE_22	Shared-Expert MoE	1 × 512 × 2304
135	Add_22_ffn	Add	1 × 512 × 2304
136	RMSNorm_23_1	RMSNorm	1 × 512 × 2304
137	Attention_23	Grouped Query Attn	1 × 512 × 2304
138	Add_23_attn	Add	1 × 512 × 2304
139	RMSNorm_23_2	RMSNorm	1 × 512 × 2304
140	MoE_23	Shared-Expert MoE	1 × 512 × 2304
141	Add_23_ffn	Add	1 × 512 × 2304
142	RMSNorm_24_1	RMSNorm	1 × 512 × 2304
143	Attention_24	Grouped Query Attn	1 × 512 × 2304
144	Add_24_attn	Add	1 × 512 × 2304
145	RMSNorm_24_2	RMSNorm	1 × 512 × 2304
146	MoE_24	Shared-Expert MoE	1 × 512 × 2304
147	Add_24_ffn	Add	1 × 512 × 2304
148	RMSNorm_25_1	RMSNorm	1 × 512 × 2304
149	Attention_25	Grouped Query Attn	1 × 512 × 2304
150	Add_25_attn	Add	1 × 512 × 2304
151	RMSNorm_25_2	RMSNorm	1 × 512 × 2304
152	MoE_25	Shared-Expert MoE	1 × 512 × 2304
153	Add_25_ffn	Add	1 × 512 × 2304
154	RMSNorm_26_1	RMSNorm	1 × 512 × 2304
155	Attention_26	Grouped Query Attn	1 × 512 × 2304
156	Add_26_attn	Add	1 × 512 × 2304
157	RMSNorm_26_2	RMSNorm	1 × 512 × 2304
158	MoE_26	Shared-Expert MoE	1 × 512 × 2304
159	Add_26_ffn	Add	1 × 512 × 2304
160	RMSNorm_27_1	RMSNorm	1 × 512 × 2304
161	Attention_27	Grouped Query Attn	1 × 512 × 2304
162	Add_27_attn	Add	1 × 512 × 2304
163	RMSNorm_27_2	RMSNorm	1 × 512 × 2304
164	MoE_27	Shared-Expert MoE	1 × 512 × 2304
165	Add_27_ffn	Add	1 × 512 × 2304
166	Final_RMSNorm	RMSNorm	1 × 512 × 2304
167	LM_Head	Linear	1 × 512 × 163840
168	Output	Output	1 × 512 × 163840

What the verifier says

info27 attention layers at embedDim 2304 cache full per-head K/V: about 243 KB per token at fp16, which dominates memory at long context. Grouped-query attention (e.g. 8:1) would cut this ~8×; multi-head latent attention (MLA) shrinks it ~10× or more. This is the move production LLMs make; it does not change the parameter count. Fix: Switch attention to groupedQueryAttention (set numKVHeads below numHeads, e.g. numHeads/4) or mla (a low-rank cached latent).
full-mha-serving-cost

infoAt 27 stacked attention layers, residual-branch outputs add up; unscaled init lets activation variance grow with depth. GPT-2/LLaMA-family models scale the residual projections by depth (N(0, 0.02 / √(2L))). Fix: Scale residual output projections by depth: nn.init.normal_(w, std=0.02 / math.sqrt(2 * n_layers))
deep-attention-default-init

Do this to your own model

Same numbers, on a model in your repo, in one command. No account.

pip install neurarch-trace
neurarch-trace moonshotai/Kimi-Linear-48B-A3B-Instruct --plan --share

Paste a model instead Markdown twin Index JSON POST /api/v1/plan